from sklearn.datasets import fetch_20newsgroups  # 从sklearn.datasets里导入新闻数据抓取器 fetch_20newsgroups
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer  # 从sklearn.feature_extraction.text里导入文本特征向量化模块
from sklearn.naive_bayes import MultinomialNB     # 从sklean.naive_bayes里导入朴素贝叶斯模型
from sklearn.metrics import classification_report


#1.数据获取
# news = fetch_20newsgroups(subset='all')
# print(len(news.data))  # 输出数据的条数：18846
# print(news[:10])

# #2.数据预处理：训练集和测试集分割，文本特征向量化
# X_train,X_test,y_train,y_test = train_test_split(news.data,news.target,test_size=0.25,random_state=33) # 随机采样25%的数据样本作为测试集
# #print X_train[0]  #查看训练样本
# #print y_train[0:100]  #查看标签
#
# #文本特征向量化
# vec = CountVectorizer()
# X_train = vec.fit_transform(X_train)
# X_test = vec.transform(X_test)
#
# #3.使用朴素贝叶斯进行训练
# mnb = MultinomialNB()   # 使用默认配置初始化朴素贝叶斯
# mnb.fit(X_train,y_train)    # 利用训练数据对模型参数进行估计
# y_predict = mnb.predict(X_test)     # 对参数进行预测
#
# #4.获取结果报告
# print('The Accuracy of Naive Bayes Classifier is:', mnb.score(X_test,y_test))
# print(classification_report(y_test, y_predict, target_names = news.target_names))


n = 3**5
print(n)
print(n%3)
print(n%5)
# print(n%3)
# print(n%4)
# print(n%5)